'throughput' 검색 결과

Deploy Suricata to detect suspicious network traffic or block it inline. Configure protocol inspection, detection rules, and structured EVE JSON logs, then connect logs to analysis platforms and check packet drops and alert quality during operation.

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• Deploying network intrusion monitoring
• Configuring inline blocking of malicious traffic
• Adding custom detection rules and reducing noisy alerts

Guides large language model reinforcement learning with miles, covering environment setup, low-precision training, consistency between training and inference, and speculative decoding, where a smaller model proposes tokens for a larger model to verify.

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• Configure reinforcement learning for large MoE models
• Investigate instability in FP8 training
• Set up speculative decoding for faster rollout generation

Configure large language model training with NVIDIA Megatron-Core across multiple GPUs. This skill helps choose how to split computation, set training parameters, monitor GPU efficiency, and address memory limits or unstable training.

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• Planning multi-GPU training for a LLaMA-style model
• Distributing MoE model experts across GPUs
• Tuning training throughput and GPU utilization

Review engineering delivery plans, hiring proposals, and team changes through six operational questions. Assess delivery metrics, recruitment stages, management structure, and production practices, then produce a verdict and three concrete actions to address the findings.

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• Reviewing quarterly delivery commitments
• Checking an engineering hiring plan
• Assessing a proposed team reorganization

Assess startup engineering operations across delivery, hiring, team structure, and production practices. Use delivery metrics, hiring conversion rates, and staffing data to identify bottlenecks, estimate candidate needs, and plan management roles and operational improvements.

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• Find where development work waits before release
• Estimate candidate volume needed for hiring goals
• Review team structure and manager hiring needs

Map repeatable business processes by owner and analyze time spent working, waiting, or reworking. Using stage-level timing data, produce a process diagram, ranked bottlenecks, and cycle-time measures to focus improvements on the main constraint.

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• Documenting procurement and employee onboarding workflows
• Identifying bottlenecks in slow business processes
• Making waits between teams visible

Generate images from text or reference images using multiple image services. Choose aspect ratios, sizes, and quality settings, or generate a collection of images in batches from saved prompt files.

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• Create illustrations from text descriptions
• Change scenes using reference images
• Generate images in specific aspect ratios

Investigate stalled TPU training across multiple slices on GKE using hang reports, logs, and latency metrics. Separate compilation inconsistencies and data input stalls from hardware or network faults, and identify the relevant nodes for further investigation.

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• Investigating HANG_DETECTED logs during TPU training
• Separating input stalls from hardware faults
• Mapping reported faulty instances to GKE nodes

Assess Google Cloud workloads for environmental sustainability using the Well-Architected Framework. Get recommendations on lower-carbon regions, efficient computing and storage, AI energy use, and ongoing emissions measurement with Carbon Footprint.

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• Reviewing cloud architecture for environmental impact
• Comparing deployment regions by carbon-free energy use
• Reducing energy waste in AI and computing workloads

Helps choose and tune Cloud Storage FUSE mounts, which expose bucket objects through file paths. It covers cache sizing, training performance, checkpoint write safety, and cost diagnosis on GKE, Compute Engine, and Cloud Run.

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• Access training data through file paths
• Choose between FUSE, native reads, and shared file systems
• Check checkpoint and concurrent write safety

Deploy AI models on GKE with suitable GPUs or TPUs and model servers. Generate deployment configuration, adjust automatic scaling, and tune inference—the process of producing model responses—for memory use, throughput, and response time.

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• Deploying Gemma or Llama on GKE
• Choosing GPUs or TPUs for model serving
• Configuring automatic scaling for LLM requests

Find relevant Google Cloud monitoring metrics for a service or resource. Retrieves current metric definitions through the API, filters them by the requested concepts, and presents selected metrics with their names, data types, units, and monitored resources.

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• Find CPU metrics for Compute Engine
• Identify Cloud Storage write throughput and request metrics
• Check metric data types and units

Configure large language model training across multiple NVIDIA GPUs with Megatron-Core. The skill helps choose how to split models and workloads, set training parameters, monitor performance, and address memory shortages or low GPU utilization.

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• Planning distributed training for a LLaMA-style model
• Distributing MoE expert networks across GPUs
• Tuning GPU utilization and training throughput

Guides large language model reinforcement learning with miles, including MoE models that route work to specialized components. Covers low-precision training, matching training and inference behavior, and accelerating training response generation with draft models.

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• Setting up reinforcement learning for large MoE models
• Configuring FP8 or INT4 training
• Checking consistency between training and inference

Estimate GPU memory, training step time, checkpoint costs, and recovery overhead for training or reinforcement learning. Compare state partitioning, recomputation, offloading, and device counts to assess whether a job can finish within its deadline.

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• Check whether training can finish by a deadline
• Compare memory-saving methods and their time costs
• Plan checkpoint intervals and recovery overhead

Compare ways to distribute AI inference across multiple instances, including replicas and separate input-processing and generation stages. Estimate capacity, throughput, delays, state-transfer costs, and recovery overhead to support routing and scaling decisions.

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• Compare replicas with separate inference stages
• Plan routing around compute capacity and cached state
• Evaluate expert placement and load imbalance

Estimate cross-server traffic, network bottlenecks, and synchronization waits for AI training or inference. Map communication onto physical network paths and assess how topology or communication changes affect the time needed to complete the job.

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• Find communication bottlenecks in distributed AI workloads
• Compare network topologies and communication paths
• Estimate synchronization waits under concurrent traffic

Diagnose why MSBuild builds across multiple projects fail to run efficiently in parallel. Use build logs and project dependencies to identify blocking chains, then recommend changes to references, scheduling, or parallel execution settings.

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• Investigate builds that do not get faster with -m
• Identify project dependency chains that delay builds
• Review whether graph builds fit a solution

Predict structures of protein, nucleic acid, and small-molecule complexes with ESMFold2, using single sequences or aligned related sequences. The skill also covers ESMC models for numerical sequence representations, mutation scoring, and predictions of contacts between residues.

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• Predicting complex structures from single sequences
• Assessing designed binders with ESMFold2-Fast
• Predicting structures using aligned related sequences

Model queues, production lines, logistics, and other systems that share limited resources in Python. Use discrete-event simulation, which advances through individual events, to measure waiting times, resource use, and throughput when comparing staffing or equipment levels.

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• Comparing queue times for different staffing levels
• Examining production line bottlenecks
• Testing inventory and logistics flows

Profile a named JAX, Levanter, or Marin run, or investigate a measured startup, compilation, initialization, or throughput bottleneck.

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Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.

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Representative, point-in-time MoE training playbooks by hardware and model family. Use them as candidate seeds, then revalidate the exact runtime, semantics, topology, and steady-state throughput.

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Use when measuring Jetson Video Codec SDK or PyNvVideoCodec encode/decode throughput, comparing presets or surfaces, testing codec-worker capacity with authenticated samples and user media, or producing a clearly labeled documentation-derived planning estimate when representative media is absent. Also use for a video request limited to PSNR or SSIM, to apply the terminal scope response.

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